Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors

Abstract

This investigation is one in a series of studies that address the possibility of stroke rehabilitation using robotic devices to facilitate "adaptive training." Healthy subjects, after training in the presence of systematically applied forces, typically exhibit a predictable "after-effect." A critical question is whether this adaptive characteristic is preserved following stroke so that it might be exploited for restoring function. Another important question is whether subjects benefit more from training forces that enhance their errors than from forces that reduce their errors. We exposed hemiparetic stroke survivors and healthy age-matched controls to a pattern of disturbing forces that have been found by previous studies to induce a dramatic adaptation in healthy individuals. Eighteen stroke survivors made 834 movements in the presence of a robot-generated force field that pushed their hands proportional to its speed and perpendicular to its direction of motion--either clockwise or counterclockwise. We found that subjects could adapt, as evidenced by significant after-effects. After-effects were not correlated with the clinical scores that we used for measuring motor impairment. Further examination revealed that significant improvements occurred only when the training forces magnified the original errors, and not when the training forces reduced the errors or were zero. Within this constrained experimental task we found that error-enhancing therapy (as opposed to guiding the limb closer to the correct path) to be more effective than therapy that assisted the subject.

Tools for understanding and optimizing robotic gait training

“…Initial work with computational models of motor training following SCI suggests that assisting in movement only as needed will be more effective than providing a fixed amount of assistance, because assistance-as-needed can limit stepping errors, while still allowing learning of a novel sensorimotor transformation. If larger stepping errors are tolerable, then amplifying errors may accelerate learning, as reviewed here for stepping experiments with nondisabled subjects [28] and elsewhere for reaching movements by nondisabled and stroke subjects [40][41].…”

Muscle co-contraction patterns in robot-mediated force field learning to guide specific muscle group training

“…The direction of the movement influences the pattern of co-contraction, but not all movements are easily achievable for populations with motor deficits. Manipulating the direction of the force instead, that has already proved effective in kinematic results (Patton,5 Stoykov, Kovic, & Mussa-Ivaldi, 2006), may be a promising rehabilitation protocol to train movement with use of a co-contraction/co-contraction reduction strategy. Force field learning paradigms provide a well described procedure to evoke and test muscle co-contraction.…”

Bridging the gap between robotic technology and health care

“…This is perhaps the reason why most of the research to date concentrates on the principle of massed practice. Robotic therapy is appealing because it can deliver complex therapies that would be too difficult for therapists to do, for instance provision of precise repeatable force and haptic feedback coupled with interesting and motivating visual feedback andor the ability to augment movement errors to help correct a movement pattern [34].…”